Doctor of Philosophy (Ph.D.)
Chemistry
University of Alberta
2014
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Faculty
Faculty
Mirza Galib, Ph.D.
Assistant Professor
Department/Office
- Chemistry
School/College
- College of Arts & Sciences
Biography
Mirza Galib, Ph.D. is an assistant professor in the Department of Chemistry at Howard University. He received his B.S. and M.S. in Applied Chemistry from the University of Dhaka (Bangladesh), and his Ph.D. in Chemistry from the University of Alberta (Canada). He also worked as a post-doctoral research associate at Pacific Northwest National Lab, UC Berkeley, and the University of Louisville. In 2022, he has started his independent career as an Assistant Professor in the Chemistry Department at Howard University. His research expertise and interest are in developing and employing computational tools based on statistical mechanics, quantum mechanics, and machine learning to study fundamental details of molecular properties in complex environments. Specific areas of current interest include understanding concentrated electrolytes and solid-electrolyte interfaces relevant to basic energy science, condensed phase materials relevant to neuromorphic computing, and air-water interfaces relevant to atmospheric chemistry.
CONTACT:
Room -103 B, Chemistry Building (1st Floor)
525 College Street NW, Washington, D.C. 20059
Education & Expertise
Education
Master of Science (M.S.)
Applied Chemistry
University of Dhaka
2007
Bachelor of Science (B.S.)
Applied Chemistry
University of Dhaka
2005
Academics
Academics
CHEM003
General Chemistry and Recitation. 4 credit lecture course. Deals with the fundamental principles of chemistry, the chemical and physical properties of the elements and their most common compounds, and methods of qualitative inorganic analysis.
CHEM004
General Chemistry and Recitation. 4 credit lecture course, it is a continuation of CHEM 003. Prerequisite: CHEM 003.
Research
Research
Specialty
Classical and quantum molecular dynamics simulations and machine learning.Group Information
Our molecular simulation group is interested in developing and employing computational tools based on statistical mechanics, quantum mechanics, and machine learning to study fundamental details of molecular properties in complex environments. Specific areas of current interest include understanding concentrated electrolytes and solid-electrolyte interfaces relevant to basic energy science, condensed phase materials relevant to neuromorphic computing, and air-water interfaces relevant to atmospheric chemistry.
A. Machine learned force field from DFT data:
Molecular dynamics simulation based on Density functional theory is an effective way to study systems where polarization plays an important role or chemical bond breaking and formation are involved. However, the computational cost of treating a large system ( having more than a few hundred atoms) with explicit atoms is currently prohibitively expensive and beyond the routine practice. In order to solve this bottleneck, we are currently using machine learning techniques (artificial neural network) to learn the forces and energies generated by appropriate density functional theory. When properly trained, the machine-learned force field can produce the accuracy of the DFT functional but provide a few orders of magnitude faster way of calculating energy and forces. This faster calculation can afford simulating complex molecular phenomena on a quantum level that was not possible to explore previously.
B. Understanding solid-liquid interface in Li-ion batteries:
Electrode-electrolyte interface plays a critical role in the design of energy storage devices. Concentrated electrolytes ( e.g. ionic liquids) are promising electrolytes in Li and beyond-Li ion batteries. We employ machine learning force field and statistical mechanical tools to understand the structure and dynamics of graphite and metal electrode with ionic liquids and leverage that understanding to design new electrolytes with improved performance.
C. Designing intermetallic alloy nano-particles for electro-catalysis:
Metal nanoparticles are promising electrocatalysts for many electrochemical reactions e.g. oxygen reduction reaction, hydrogen evolution reaction, and CO2 reduction reaction. Platinum-group metals are the most commonly used electrocatalysts which are scarce and costly. Intermetallic nanoparticles alloy where low-cost transition metals can be mixed with platinum-group metal is a promising new technology to reduce the use of costly metals. We employ machine learning, quantum mechanics, and statistical mechanics to understand molecular level details and to build structure-activity correlation in intermetallic nanoparticles alloy and consequently pave the way for designing new electrocatalysts.
We are currently recruiting Ph.D. students and post-doctoral research associates in our group. Interested candidates are welcome to contact me via mirza.galib@howard.edu.
Publications: ( h-index 12, citations 656)
( https://scholar.google.com/citations?user=ymI99ZAAAAAJ&hl=en )
Publications and Presentations
Publications and Presentations
Controlled Formation of Conduction Channel in Memristive Devices Observed by X-ray Multimodal Imaging.
Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X-ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. This work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing.
Uptake of N2O5 by aqueous aerosol unveiled using chemically accurate many-body potentials
Uptake of N2O5 by aqueous aerosol unveiled using chemically accurate many-body potentials
Here we use molecular dynamics simulations with a data-driven many-body model of coupled-cluster accuracy to quantify thermodynamics and kinetics of solvation and adsorption of N2O5 in water. The free energy profile highlights that N2O5 is selectively adssorbed to the liquid–vapor interface and weakly solvated. Accommodation into bulk water occurs slowly, competing with evaporation upon adsorption from gas phase. Leveraging the quantitative accuracy of the model, we parameterize and solve a reaction–diffusion equation to determine hydrolysis rates consistent with experimental observations.
Reactive uptake of N2O5 by atmospheric aerosol is dominated by interfacial process
Reactive uptake of N2O5 by atmospheric aerosol is dominated by interfacial process
Here, we use molecular simulations, including reactive potentials and importance sampling, to study the uptake of N2O5 into an aqueous aerosol. Rather than being mediated by the bulk, uptake is dominated by interfacial processes due to facile hydrolysis at the liquid-vapor interface and competitive reevaporation. With this molecular information, we propose an alternative interfacial reactive uptake model consistent with existing experimental observations.
Supersaturated calcium carbonate solutions are classical
Supersaturated calcium carbonate solutions are classical
Mechanisms of CaCO3 nucleation from solutions that depend on multistage pathways and the existence of species far more complex than simple ions or ion pairs have recently been proposed. Herein, we provide a tightly coupled theoretical and experimental study on the pathways that precede the initial stages of CaCO3 nucleation. Starting from molecular simulations, we succeed in correctly predicting bulk thermodynamic quantities and experimental data, including equilibrium constants, titration curves, and detailed x-ray absorption spectra taken from the supersaturated CaCO3 solutions. The picture that emerges is in complete agreement with classical views of cluster populations in which ions and ion pairs dominate, with the concomitant free energy landscapes following classical nucleation theory.